Feature interaction maximisation

Pattern Recognition Letters

Volumn 34, Issue 14, 2013, Pages 1630-1635

  Bennasar, Mohamed ^a   Setchi, Rossitza ^a   Hicks, Yulia ^a  

^a CARDIFF UNIVERSITY   (United Kingdom)

Author keywords

Classification; Dimensionality reduction; Feature selection; Interaction information; Mutual information; Subset feature selection

Indexed keywords

FEATURE EXTRACTION; NEAREST NEIGHBOR SEARCH; REDUNDANCY;

CLASSIFICATION ACCURACY; CLASSIFICATION ALGORITHM; DIMENSIONALITY REDUCTION; FEATURE SELECTION METHODS; INTERACTION INFORMATION; MINIMUM REDUNDANCY-MAXIMUM RELEVANCES; MUTUAL INFORMATIONS; THREE-WAY INTERACTIONS;

CLASSIFICATION (OF INFORMATION);

EID: 84885659095     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2013.04.002     Document Type: Article

References (22)

1. Battiti, R., 1994. Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Networks 5, 537-550.
2. Brown, G., Pocock, A., Zhao, M., Lujan, M., 2012. Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J. Mach. Learn. Res. 13, 27-66.
3. Cheng, H., Qin, Z., Feng, C., Wang, Y., Li, F., 2011. Conditional mutual informationbased feature selection analysing for synergy and redundancy. ETRI J. 33, 210-218.
4. Cover, T., Thomas, J., 2006. Elements of Information Theory, second ed. John Wiley & Sons.
5. Ding, C., Peng, H., 2003. Minimum redundancy feature selection from microarray gene expression data. In: Proceedings of the Computational Systems Bioinformatics (CSB '03), IEEE Computer Society.
6. Duda, R., Hart, P., Stork, D., 2001. Pattern Classification, second ed. John Wiley and Sons, New York.
7. El Akadi, A., El Ouardighi, A., Aboutajdine, D., 2008. A powerful feature selection approach based on mutual information. IJCSNS Int. J. Comput. Sci. Network Secur. 8, 116-121.
8. Frank, A., Asuncion, A., 2010. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA, University of California, School of Information and Computer Science.
9. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L., 2006. Feature Extraction Foundations and Applications. Springer, Heidelberg.
10. Guyon, I., Elisseeff, A., 2003. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157-1182.
11. Jakulin, A., 2003. Attribute Interactions in Machine Learning. Master thesis, Computer and Information Science, University of Ljubljana. Jakulin, A., 2005. Machine Learning Based on Attribute Interactions. PhD thesis, Computer and Information Science, University of Ljubljana.
12. Karegowda, A., Jayaram, A.M., Manjunath, A., 2010. Feature subset selection problem using wrapper approach in supervised learning. Int. J. Comput. Appl. 1, 13-17.
13. Kira, K., Rendell, L., 1992. A practical approach to feature selection. A practical approach to feature selection. In: ML92 Proceedings of the Ninth International Workshop on, Machine Learning, pp. 249-256.
14. Kwak, N., Choi, C., 2002. Input feature selection for classification problems. IEEE Trans. Neural Networks 13, 143-159. (Pubitemid 34236844)
15. Lee, J., Kim, D., 2013. Feature selection for multi-label classification using multivariate mutual information. Pattern Recogn. Lett. 34, 349-357.
16. Lin, T., Li, H., Tsai, K.-C., 2004. Implementing the Fisher's discriminant ratio in a kmeans clustering algorithm for feature selection and data set trimming. J. Chem. Inf. Comput. Sci. 44, 76-87.
17. Liu, H., Motoda, H., 2008. Computational Methods of Feature Selection. Chapman and Hall/CRC, Boca Raton.
18. Liu, H., Sun, J., Liu, L., Zhang, H., 2009. Feature selection with dynamic mutual information. Pattern Recogn. 42, 1330-1339.
19. Meyer, P., Bontempi, G., 2006. On the use of variable complementarity for feature selection in cancer classification. In: Applications of Evolutionary Computing: EvoWorkshops, pp. 91-102. (Pubitemid 44019198)
20. Meyer, P., Schretter, C., Bontempi, G., 2008. Information-theoretic feature selection in microarray data using variable complementarily. IEEE J. Sel. Top. Signal Process. 2, 261-274.
21. Peng, H., Long, F., Ding, C., 2005. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226-1238. (Pubitemid 41245053)
22. Rodgers, J., Nicewander, W., 1988. Thirteen ways to look at the correlation coefficient. Am. Stat. 42, 59-66.